Surgical robotic system having anthropometry-based user console

ABSTRACT

Surgical robotic systems including a user console for controlling a robotic arm or a surgical robotic tool are described. The user console includes components designed to automatically adapt to anthropometric characteristics of a user. A processor of the surgical robotic system is configured to receive anthropometric inputs corresponding to the anthropometric characteristics and to generate an initial console configuration of the user console based on the inputs using a machine learning model. Actuators automatically adjust a seat, a display, or one or more pedals of the user console to the initial console configuration. The initial console configuration establishes a comfortable relative position between the user and the console components. Other embodiments are described and claimed.

BACKGROUND Field

Embodiments related to surgical robotic systems are disclosed. Moreparticularly, embodiments related to automatically adjusting userconsole configurations in surgical robotic systems are disclosed.

Background Information

Endoscopic surgery involves looking into a patient's body and performingsurgery inside the body using endoscopes and other surgical tools. Forexample, laparoscopic surgery can use a laparoscope to access and viewan abdominal cavity. Endoscopic surgery can be performed using manualtools and/or a surgical robotic system having robotically-assistedtools.

A surgical robotic system may be remotely operated by a surgeon tocommand robotically-assisted tools and at least one camera located at anoperating table. The surgeon may use a user console located in theoperating room (or in a different city) to command a robot to manipulatethe surgical tool and the camera. For example, the surgeon may hold inher hand a user input device such as a joystick or a computer mouse thatshe manipulates to generate control commands that cause motion of thesurgical robotic system components, e.g., the surgical tool or thecamera. The robot uses the surgical tools to perform surgery, with thevisualization aid provided by the camera.

The surgeon may be seated at the user console of the surgical roboticsystem for extended periods of time during surgery. The extendedsurgical sessions can induce fatigue and/or physical discomfort for thesurgeon. Accordingly, surgical robotic systems may have seats that thesurgeon can manually adjust to a comfortable position.

SUMMARY

Existing user consoles of surgical robotic systems allow a surgeon tomanually adjust a seat to a variety of positions. The surgeon can raise,tilt, or translate the seat to a preferred position. The preferredposition can be a position that the surgeon finds most comfortable,which may improve surgical performance, especially during lengthyoperations. Manual adjustability of the seat, however, may introduceseveral unnoticed and/or undesirable effects. The surgeon may waste timeor spend too long adjusting the seat because the seat adjustability mayhave many degrees of freedom for requiring adjustment. The surgeon mayfail to find an optimal configuration, i.e., a “sweet spot,” becausealthough the surgeon may feel comfortable after initially adjusting theseat position, the seat position may actually be a temporarily optimalposition. That is, the seat position may induce more user fatigue overlong periods of time than would a different, long-term, optimal positionwould. The difference between the temporarily optimal position and thelong-term optimal position, however, may be unnoticeable to the surgeonat the time of adjustment.

Surgical robotic systems including a user console having a seat, adisplay, and/or one or more pedals designed to automatically adapt toanthropometric characteristics of a user are described. It has beendetermined that there are correlations between anthropometriccharacteristics of individuals and the optimal setup of the user consolefor those individuals, including user/seat poses, distances and anglesof the display or pedals, and positions of the display or pedalsrelative to the user/seat poses. The user console includes a processorconfigured to generate a recommended/initial configuration based onanthropometric inputs corresponding to physical attributes of the user.The processor can use a machine learning (ML) model to predict optimalpositions and poses of the seat, display, or pedals based on theidentified physical attributes. The output configuration may be used bythe processor to drive one more console actuators to adjust one or moreof the seat, the display, or the pedals to the recommended initialconfiguration. The initial configuration is a predicted optimal settingsof the user console for long-term use.

In an embodiment, one or more actuators of the user console can adjustthe user console to the recommended/initial configuration. The initialconfiguration establishes a comfortable relative position between theuser and one or more components of the surgical robotic system. Forexample, the initial console configuration can position a face of theuser relative to the display in a manner that facilitates correct andcomfortable viewing of the display. By way of example, a viewingdistance or a viewing angle of the user can be adjusted by movement ofthe seat to the initial console configuration.

In an embodiment, the ML model receives a limited number of inputscorresponding to physical attributes of the user to derive therecommended console configuration. For example, the user may enterinformation that is readily known, such as a height, shoe size, orgender of the user. The ML model may include multi-stage ML models togenerate the output configurations. For example, a first ML model cancorrelate the limited input data to a broader set of physical attributedata including information that is not readily known, such as the user'sarm length or lower leg length. A second ML model can correlate thebroader set of data output by the first ML model to the consoleconfigurations. Accordingly, surgical robotic system can recommendconsole settings using the ML models and automatically set up theconsole components to a recommended initial configuration based on entryof only a few well-known parameters.

The above summary does not include an exhaustive list of all aspects ofthe present invention. It is contemplated that the invention includesall systems and methods that can be practiced from all suitablecombinations of the various aspects summarized above, as well as thosedisclosed in the Detailed Description below and particularly pointed outin the claims filed with the application. Such combinations haveparticular advantages not specifically recited in the above summary.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the invention are illustrated by way of example andnot by way of limitation in the figures of the accompanying drawings inwhich like references indicate similar elements. It should be noted thatreferences to “an” or “one” embodiment of the invention in thisdisclosure are not necessarily to the same embodiment, and they mean atleast one. Also, in the interest of conciseness and reducing the totalnumber of figures, a given figure may be used to illustrate the featuresof more than one embodiment of the invention, and not all elements inthe figure may be required for a given embodiment.

FIG. 1 is a pictorial view of a surgical robotic system in an operatingarena, in accordance with an embodiment.

FIG. 2 is a schematic view of physical attributes of a user, inaccordance with an embodiment.

FIG. 3 is a schematic view of a user in a seated pose at a user consoleof a surgical robotic system, in accordance with an embodiment.

FIGS. 4A-4B are flowcharts of methods of adjusting configurations of auser console of a surgical robotic system, in accordance with anembodiment.

FIG. 5 is a block diagram of a single-stage machine learning model, inaccordance with an embodiment.

FIG. 6 is a block diagram of a multi-stage machine learning model, inaccordance with an embodiment.

FIG. 7 is a schematic view of a user in a reclined pose at a userconsole of a surgical robotic system, in accordance with an embodiment.

FIG. 8 is a schematic view of a user in a standing pose at a userconsole of a surgical robotic system, in accordance with an embodiment.

FIG. 9 is a block diagram of a surgical robotic system, in accordancewith an embodiment.

DETAILED DESCRIPTION

Embodiments describe a surgical robotic system having a user console forcontrolling a robot. The user console includes adjustable components,such as a seat, a display, and/or one or more pedals which areautomatically adjustable to an optimal position for a current user. Theuser console having adjustable components may be incorporated in othersystems, such as aircraft systems or automobile systems, to name only afew possible applications.

In various embodiments, description is made with reference to thefigures. However, certain embodiments may be practiced without one ormore of these specific details, or in combination with other knownmethods and configurations. In the following description, numerousspecific details are set forth, such as specific configurations,dimensions, and processes, in order to provide a thorough understandingof the embodiments. In other instances, well-known processes andmanufacturing techniques have not been described in particular detail inorder to not unnecessarily obscure the description. Reference throughoutthis specification to “one embodiment,” “an embodiment,” or the like,means that a particular feature, structure, configuration, orcharacteristic described is included in at least one embodiment. Thus,the appearance of the phrase “one embodiment,” “an embodiment,” or thelike, in various places throughout this specification are notnecessarily referring to the same embodiment. Furthermore, theparticular features, structures, configurations, or characteristics maybe combined in any suitable manner in one or more embodiments.

The use of relative terms throughout the description may denote arelative position or direction. For example, “forward” may indicate afirst direction away from a reference point, e.g., in front of a seat.Similarly, “backward” may indicate a location in a second directionopposite to the first direction, e.g., behind the seat. Such terms areprovided to establish relative frames of reference, however, and are notintended to limit the use or orientation of a surgical robotic system toa specific configuration described in the various embodiments below.

It has been discovered that there are correlations between auser-preferred configuration of a user console and physical attributesof the user. For example, a seating configuration, which may be definedby a seat lift, a seat recline, or a seat translation parameter, or adisplay configuration, which may be defined by a display lift or adisplay tilt parameter, correlates with a height, arm length, leglength, shoe size, etc., of the user. In an aspect, a surgical roboticsystem predicts an optimal initial console configuration of a seat, adisplay, or another system component such as one or more pedals, for auser based on the anthropometric characteristics of the user. Thesurgical robotic system includes a processor configured to receiveanthropometric inputs (or parameters) corresponding to theanthropometric characteristics and to recommend initial (or optimal)console configurations using a machine learning (ML) model. For example,the ML model may include at least two regression ML models arranged in aserial order. Actuators of the console system (e.g., seat actuators),can adjust respective components based on the recommended consoleconfiguration. The initial console configuration may be a predictedpreferred position of the user, and can establish a relative viewingposition and angle between the user and the display of the user console.By optimizing the initial console configuration, fatigue and/ordiscomfort of the user may be reduced during long surgeries. Greatercomfort can lead to better surgical performance and surgical outcomes.

Referring to FIG. 1, a pictorial view of a surgical robotic system in anoperating arena is shown in accordance with an embodiment. A surgicalrobotic system 100 comprises a user console 120, a control tower 130,and one or more robotic arms 112 located at a surgical platform 111,e.g., mounted on a table, a bed, etc. Surgical robotic system 100 canincorporate any number of devices, tools, or accessories used to performsurgery on a patient 102. For example, surgical robotic system 100 mayinclude one or more surgical tools 104 used to perform surgery. Surgicaltools 104 can be end effectors attached to distal ends of robotic arms112 for executing a surgical procedure.

Each surgical tool 104 can be manipulated manually and/or roboticallyduring the surgery. For example, surgical tool 104 may be a tool used toenter, view, or manipulate an internal anatomy of patient 102. In anembodiment, surgical tool 104 is a grasper used to grasp tissue ofpatient 102. Surgical tool 104 can be manually handled by a bedsideoperator 106, or robotically controlled by movement of robotic arms 112.Robotic arms 112 are shown as a table-mounted system, but in otherconfigurations, robotic arms 112 may be mounted in a cart, ceiling orside wall, or other suitable support surface within the operating arena.

Generally, a user 107, such as a surgeon or other operator, may use userconsole 120 to remotely manipulate robotic arms 112 and/or surgicaltools 104, e.g., by tele-operation. User console 120 may be located inthe same operating arena or room as robotic arms 112 as shown in FIG. 1.In other environments, user console 120 may be located in an adjacent ornearby room, or tele-operated from a remote location in a differentbuilding, city, or country. User console 120 may comprise a seat 122,one or more foot-operated control pedals 124, one or more handheld userinterface devices (UIDs) 126, and at least one user display 128configured to display, for example, a view of the surgical site inside apatient. As shown in the exemplary user console 120, user 107 located inseat 122 and viewing user display 128 may manipulate pedals 124 and/orhandheld UIDs 126 to remotely command robotic arms 112 and/or surgicaltools 104 mounted on the distal ends of robotic arms 112.

In some variations, bedside operator 106 may also operate the surgicalrobotic system 100 in an “over the bed” mode, in which the user is at aside of patient 102 and simultaneously manipulating a robotically-driventool/end effector attached thereto, e.g., with a UID 126 held in onehand, and a manual laparoscopic tool. For example, the bedsideoperator's left hand may be manipulating a handheld UID 126 to command asurgical robotic component, while the bedside operator's right hand maybe manipulating a manual laparoscopic tool. Thus, in these variations,bedside operator 106 may perform both robotic-assisted minimallyinvasive surgery and manual laparoscopic surgery on patient 102.

During an exemplary procedure or surgery, patient 102 is prepped anddraped in a sterile fashion to achieve anesthesia. Initial access to thesurgical site may be performed manually with the surgical robotic system100 in a stowed configuration or withdrawn configuration to facilitateaccess to the surgical site. Once access is completed, initialpositioning and/or preparation of the surgical robotic system may beperformed. During the procedure, user 107 in user console 120 mayutilize pedals 124 and/or UID 122 to manipulate various end effectorsand/or imaging systems to perform the surgery. Manual assistance mayalso be provided at the procedure table by sterile-gowned bedsidepersonnel, e.g., bedside operator 106, who may perform tasks includingbut not limited to, retracting tissues or performing manualrepositioning or tool exchange involving one or more robotic arms 112.Non-sterile personnel may also be present to assist user 107 at userconsole 120. When the procedure or surgery is completed, surgicalrobotic system 100 and/or user console 120 may be configured or set in astate to facilitate one or more post-operative procedures, including butnot limited to cleaning and/or sterilization, and/or healthcare recordentry or printout, whether electronic or hard copy, such as via userconsole 120.

In an embodiment, user 107 holds and moves UID 126 to provide an inputcommand to move one or more actuators 114 of surgical robotic system100. UID 126 may be communicatively coupled to surgical robotic system100, e.g., via console computer system 110. UID 126 can generate spatialstate signals corresponding to movement of UID 126, and the spatialstate signals may be input to control motions of actuators 114 ofsurgical robotic system 100. Surgical robotic system 100 may use thecontrol signals to control proportional motions of actuators 114. Forexample, a console processor of console computer system 110 can receivethe spatial state signals and generate corresponding robot controlsignals that are output to robotic arms 112. Actuators 114 may becoupled to a corresponding surgical tool 104, and thus, thecorresponding surgical tool 104 may be moved by the correspondingactuators 114 based on the robot control signals to mimic movement ofUID 126. Similarly, interaction between user 107 and UID 126 cangenerate a grip signal to cause a jaw of a grasper of surgical tool 104to close and grip tissue of patient 102.

In some aspects, communication between surgical platform 111 and theuser console 120 may be through control tower 130, which may translateuser commands from user console 120 to robotic control commands andtransmit the converted commands to surgical platform 111. Control tower130 may also transmit status and feedback from surgical platform 111back to user console 120. The connections between surgical platform 111,user console 120, and control tower 130 may be via wired and/or wirelessconnections, and may be proprietary and/or performed using any of avariety of data communication protocols. Any wired connections may beoptionally built into the floor and/or walls or ceiling of the operatingroom. Surgical robotic system 100 may provide video output to one ormore displays, including displays within the operating room as well asremote displays accessible via the Internet or other networks. The videooutput or feed may also be encrypted to ensure privacy and all orportions of the video output may be saved to a server or electronichealthcare record system.

In an embodiment, processors of surgical robotic system, e.g., a consoleprocessor (FIG. 9) of user console 120, can receive user inputs andgenerate corresponding console configurations to drive one or moreactuators of user console 120. The actuators of user console 120 can beassociated with each of seat 122, display 128, or pedals 124 of surgicalrobotic system 100. For example, user console 120 can include one ormore actuators 132 mechanically connected to seat 122 to causehorizontal translation, tilting, vertical movement, etc., of one or morecomponents of seat 122. Similarly, display actuators can cause movementof display 128, and pedal actuators can cause movement of pedals 124.The actuator-caused movements of seat 122, display 128, and pedals 124are described further below.

It will be appreciated that the operating room scene in FIG. 1 isillustrative and may not accurately represent certain medical practices.

Referring to FIG. 2, a schematic view of physical attributes of a useris shown in accordance with an embodiment. Surgical robotic system 100may be shared by several users. For example, surgical robotic system 100may be used by several surgeons at a hospital to perform differentsurgeries. The physical attributes of each user include numerousanatomical metrics. For example, each user is likely to have differentphysical characteristics and different ranges of motion. Likewise, eachuser is likely to have a different optimal initial console configurationof the system components, e.g., seat 122, display 128, and pedals 124.Accordingly, the capability to predict and automatically adjust thesystem components to accommodate the individual characteristics of eachuser provides value and mitigates the need for each user to separatelyadjust the system components when beginning a new surgery.

A processor of surgical robotic system 100, e.g., console processor, canreceive one or more anthropometric inputs (or parameters). Theanthropometric inputs include the user-specific anthropometric data,i.e., data corresponding to one or more physical attributes of user 107.It is contemplated that any physical attributes of user 107 may be inputto the processor to generate initial console configurations as describedbelow. Several physical attributes of user 107, however, are shown inFIG. 2 as examples of anthropometric characteristics associated with anoptimal position of seat 122, display 128, or pedals 124 for a givenuser.

Primary physical attributes indicative of a preferred systemconfiguration include: a height (h.) 202 of user 107 measured from theground to eye level; and a shoe size (s.s.) 204 of user 107.Anthropometric input data corresponding to shoe size 204 can be a numberand/or letter corresponding to any shoe-size system, e.g., size “11.”Alternatively, anthropometric input data may be derived from suchinformation. For example, user 107 may enter a shoe size 204, and theprocessor of surgical robotic system 100 may derive other physicalcharacteristics, such as a foot width (f.w.) 206 or a foot length (f.l.)208 from the information. A gender (g.) 207 of user 107 has also beenshown to relate closely with certain system configuration settings.Accordingly, in an embodiment, user 107 may enter information aboutpersonal physical attributes into console computer system 110 at thebeginning of a surgery, and at a minimum, the information may includeheight 202, shoe size 204, and (optionally) gender 207 of user 107.

Secondary physical attributes indicative of the preferred systemconfiguration for user 107 include: arm length (a.) 210, which may befurther broken down into upper arm length (u.a.) 212 measured from theshoulder to the elbow, and forearm length (f.a.) 214 measured from theelbow to the wrist; and leg length (l.) 216, which may be further brokendown into upper leg length (u.l.) 218 measured from the hip to the knee,and lower leg length (l.l.) 220 measured from the knee to the ankle. Thesecondary physical attributes may be independent of, but related to, theprimary physical attributes. For example, height 202 of user 107 maycorrelate to leg length 216 of user 107.

It would be cumbersome to measure all of the above-mentioned anatomicalmetrics to fully characterize the physique of user 107. Furthermore,user 107 may not know all of these physical attributes. For example,user 107 may not know his arm length 210, lower leg length 216, etc. Inan embodiment, surgical robotic system 100 can predict the optimalinitial console configuration of the console components based on asubset, e.g., two or more, of the above-mentioned anatomical metrics.For example, surgical robotic system 100 can predict the optimal initialconsole configuration based on an input of only height 202 and shoe size204, and optionally gender 207 of user 107.

Referring to FIG. 3, a schematic view of a user 107 seated at userconsole 120 of a surgical robotic system 100 is shown in accordance withan embodiment. Like the physical attributes of user 107, the degrees offreedom defining the configuration of console components may be several.For example, the console configuration may depend on numerousconfiguration parameters, as shown in FIG. 3. In an embodiment, consoleactuators, e.g., seat actuators 132, adjust respective components, e.g.,seat 122, of user console 120 to an initial console configuration 304.Initial console configuration 304 may be defined by the configurationparameters that establish a predicted optimal relative position betweenuser 107 and the console components. For example, initial consoleconfiguration 304 of seat 122 can establish a predicted optimal relativeposition between a face 302 of user 107 and display 128 of surgicalrobotic system 100. The predicted optimal relative position can also beachieved by moving display 128 to the initial console configuration 304using a display actuator. Likewise, pedals 124 may be moved into initialconsole configuration 304 by corresponding pedal actuators. Accordingly,initial console configuration 304 may be defined by one or more of apredicted optimal spatial position or orientation of seat 122, display128, or pedals 124.

In an embodiment, the configuration parameters defining the spatialposition or orientation of seat 122 include a seat translation (s.x.)parameter 306 and a seat lift (s.l.) parameter 308. Seat translationparameter 306 can be a distance measured horizontally from seat 122 to adatum associated with a frame of user console 120. For example, seattranslation parameter 306 may be the distance between seat 122 and acolumn supporting display 128 in front of seat 122. Seat lift parameter308 can be a distance measured vertically from seat 122 to a datumassociated with the frame of user console 120. For example, seat liftparameter 308 may be the distance between seat 122 and a floor thatsupports seat 122. The spatial position or orientation of seat 122 canalso be defined by a seat tilt (s.t.) parameter 310. Seat tilt parameter310 may be an angle between a horizontal plane and a plane extendingparallel to an upper surface of seat 122 on which user 107 sits. Thespatial position or orientation of seat 122 can also be defined by aseat recline (s.r.) parameter 312. Seat recline parameter 312 may be anangle between the horizontal plane and a back of seat 122 against whichuser 107 leans. Other parameters that define the spatial position ororientation of seat 122, which are omitted here for brevity, include atranslation, lift, or tilt parameter for a headrest of seat 122, anarmrest of seat 122, a lumbar support of seat 122, etc.

A spatial position or orientation of display 128 can be defined bysimilar configuration parameters, including a display lift (d.l.)parameter 318, a display translation (d.x.) parameter 320 and a displaytilt (d.t.) parameter 322. Display translation parameter 320 can be adistance measured horizontally from display 128 to a datum associatedwith a frame of user console 120. For example, display translationparameter 320 may be the distance between display 128 and a columnsupporting display 128 in front of display 128. Display tilt parameter322 may be an angle between a horizontal plane and a plane extendingparallel to a viewing plane of display 128.

A spatial position or orientation of pedals (or a panel of pedals) 124can be defined by similar configuration parameters, including a pedaltranslation (p.x.) parameter 324 and a pedal tilt (p.t.) parameter 326.Pedal translation parameter 324 can be a distance measured horizontallyfrom pedals 124 to a datum associated with a frame of user console 120.For example, pedal translation parameter 324 may be the distance betweenpedals 124 and a column supporting display 128 in front of pedals 124.Pedal tilt parameter 326 may be an angle between a horizontal plane anda plane extending parallel to a surface of pedals 124 on which a foot ofthe user rests.

The configuration parameters defined above determine the spatialposition or orientation of user 107 when the user is seated at userconsole 120. Accordingly, the configuration parameters define a relativeposition between user 107 and components of user console 210. Forexample, the relative position may be a viewing distance 330 measuredbetween face 302 of user 107 and display 128. Similarly, the relativeposition may be a viewing angle 332 measured between a plane extendingalong the viewing plane of display 128 and a plane that is normal to aline of sight of user 107. Viewing angle 332 can be described as arelative tilt between display 128 and face 302. The relative position ofuser 107 impacts a comfort of user 107 during the surgery. For example,viewing distance 330 and viewing angle 332 can affect an ability of user107 to view the display screen correctly and comfortably.

In an embodiment, user console 120 establishes as an output the optimalrelative position of user 107 based on input physical parameters of user107. That is, user 107 can enter information related to the physicalattributes described above, and the processor will predict the optimalrelative position of user 107, and set the configuration parameters tocause the console actuators to move the console components such thatuser 107 is moved into the optimal relative position. Prediction of theconfiguration parameter outputs based on the input physical parametersmay be made using machine learning. A method of predicting and adjustingconsole components (seat 122 is provided by way of example) of surgicalrobotic system 100 to the optimal initial console configuration 304 isdescribed below.

Referring to FIGS. 4A-4B, flowcharts of a method of adjustingconfigurations of a user console of a surgical robotic system are shownin accordance with an embodiment. For example, seat 122 and/or otherconsole components of a surgical robotic system 100 can be adjusted toan initial console configuration 304. Operations of the methodsillustrated in FIGS. 4A-4B correspond to the illustrations of FIGS. 5-7,and thus, FIGS. 4A-7 are described in combination below.

Referring to FIG. 5, a block diagram of a single-stage ML model 506 isshown in accordance with an embodiment. The block diagram of FIG. 5 isdescribed here in combination with the method of FIG. 4A. At operation402, a processor of surgical robotic system 100 can receive one or moreanthropometric inputs 502 corresponding to one or more physicalattributes of user 107. Anthropometric inputs 502 received or derived bythe processor can include physical characteristics of user 107 seated atuser console 120 to tele-operate a surgical robot, including: height202, gender 207, upper arm length 212, forearm length 214, upper leglength 218, lower leg length 220, foot length 208, or foot width 206 ofuser 107. Anthropometric inputs 502 can represent data entered manuallyby user 107 using input devices such as a graphical user interface, akeyboard, and/or a mouse, etc. In an embodiment, user console 120 isequipped with a vision system configured to detect and/or measurecertain physical attributes of user 107. For example, a camera of thevision system may capture an image of the user 107 and compare the imageto reference data in order to determine height 202 or shoe size 204 ofuser 107. Alternatively, user 107 may enter a unique identifier, e.g.,an employee identification number, and the processor may retrievepreviously stored physical attributes data from a database entrycorresponding to user 107.

At operation 404, the processor of surgical robotic system 100 processesthe inputs 502 using an ML model 506 to generate a set of actuationcommands for adjusting user console 120. The actuation commands caninclude console configuration parameters 504 corresponding to anoptimized (or recommended) initial console configuration based onanthropometric metrics of the user. More particularly, the set ofactuation commands can be used to adjust user console 120 to therecommended configuration based on the physical attributes of user 107.

In an embodiment, the recommended configuration effects a seating poseof user 107, e.g., a pose of seat 122, at user console 120. The outputs504 can include one or more of: display lift parameter 318, displaytranslation parameter 320, display tilt parameter 322, pedal tiltparameter 326, pedal translation parameter 324, seat translationparameter 306, seat recline parameter 312, seat lift parameter 308, orseat tilt parameter 310, which may be used by the processor to actuateone or more actuators of user console 120 to adjust the positions andangles of seat 122, display 128, and/or pedals 124. Accordingly, seatingpose is represented by one or more parameters including seat translationparameter 306, seat lift parameter 308, seat tilt parameter 310, or seatrecline parameter 312. Adjustment of the seating pose based on theoutputs 504 can effect a relative viewing distance and angle betweenuser 107 and display 128.

To generate the set of actuation commands, the processor can process theanthropometric inputs 502 using a machine learning algorithm. The MLmodels described with respect to FIGS. 5-6 can be regression models thatare trained to correlate an input to an output.

In some implementations, the ML model (506 or 606) may be trained bydata collected from hundreds of surgeons and volunteers, whoseanthropometric metrics, such as height, upper and forearm lengths, upperand lower leg lengths, and/or foot length, have been measured. Theaccumulated anthropometric data can be correlated to an output, such asconsole configuration parameters. For example, the surgeons andvolunteers that are measured to accumulate the anthropometric data maybe asked to adjust the user console to the best of their abilities. Theadjustments made by the users represent user preferences, which can berelated to the measured anthropometric data for the users. Accordingly,user preferences in the console setting, such as seat, display and pedalplacement for various seat poses, have been collected as “ground truth”for the machine training purpose. In some implementations, ML model 506(or 606) can include a regularized regression model. For example, aregularized multivariable regression algorithm can be applied to modelthe correlations between the user preferences in console settings andthe physical attributes of the users. After training the ML model, theregularized regression model can recommend a configuration of userconsole 120 based on a correlation between the physical attributes of auser and the user preferences in user console configurations. More userpreference data can be collected to test and fine tune the regressionmodel. The ML model proves to be a reliable predictor for optimuminitial console settings, and recommended initial console settings maysave precious time for surgeons in operating rooms.

In some implementations, the accumulated anthropometric data can becorrelated to additional anthropometric data (see extendedanthropometric parameters of FIG. 6). Training data can be obtained frommeasurements of users, as described above, or from published sources ofanthropometric data. The accumulated anthropometric data can be used tocorrelate an input set of anthropometric parameters to an output set ofanthropometric parameters. For example, a regularized multivariableregression algorithm can be applied to model correlations between anoutput set of parameters including one or more of upper arm length 212,forearm length 214, upper leg length 218, lower leg length 220, footlength 208, or foot width 206, to an input set of parameters includingone or more of height 202, gender 207, or shoe size 204 (see FIG. 6).

At operation 406, one or more actuators of user console 120 can beactuated based on the set of actuation commands. For example, actuatorsconnected to seat 122, display 128, and/or pedals 124 can be controlledaccording to the generated configuration of user console 120 to adjust apose of seat 122, display 128, or pedals 124, a position or angle ofseat 122, display 128, or pedals 124, or a position of seat 122, display128, or pedals 124 relative to each other or other components of userconsole 120. Seat and display adjustment can include adjusting one ormore configuration parameters, including: seat translation 306, seatlift 308, seat tilt 310, seat recline 312, display translation 320,display lift 318, or display tilt 322. Accordingly, the configuration ofuser console 120 can be adjusted.

FIG. 5 illustrates a single-stage regression model that relates theconsole configuration parameters to a set of physical attributes, whichinclude physical attributes that are not readily known or convenient tomeasure. For example, user 107 may not know his upper leg length 218,and thus, may be unable to enter every input required to generate theinitial console configuration. As described below, in an embodiment,user 107 is allowed to enter information that is readily known, and MLmodel 506 can derive information that is not readily known as inputs toa predictive model used to generate console configuration parameters504.

Referring to FIG. 6, a block diagram of a multi-stage ML model 606 isshown in accordance with an embodiment. The block diagram of FIG. 6 isdescribed here in combination with the method of FIG. 4B. At operation412, one or more anthropometric parameters 602 of user 107 are received.

In an embodiment, ML model 606 used to predict console configurationparameters 604 (similar to outputs 504) based on anthropometric inputs602 (similar to inputs 502) can include at least two stages. Themulti-stage ML model 606 can include a first ML model 614 to receiveanthropometric inputs 602, and a second ML model 616 to outputrecommended configurations 604. The ML models 614 and 616 may bearranged in a serial order. For example, first ML model 614 may output aset of extended anthropometric parameters 608 of the user based oninputs 602. The set of extended anthropometric parameters 608 may be feddirectly to second ML model 616 to generate outputs 604. Each ML model614, 616 can include respective regularized linear or nonlinearregression ML models designed to estimate or predict an output based onan input.

At operation 414, extended anthropometric parameters 608 of user 107 aregenerated based on the received anthropometric parameters 602. In anembodiment, first ML model 614 correlates a smaller set of readily knownphysical attributes to a larger set of physical attributes. For example,there are more anthropometric parameters 608 output by first ML model614 than anthropometric inputs 602. By way of example, anthropometricinputs 602 may be the primary physical attributes described above, suchas two or more of height 202, shoe size 204, or gender 207 of user 107.Such information is generally readily known by users. Accordingly, user107 can enter the information and the anthropometric parameters can bereceived from the user for processing. In an embodiment, theanthropometric inputs 602 entered into first ML model 614 include justheight 202 and shoe size 204 of user 107. Anthropometric inputs 602 cantherefore be a subset of inputs 502 used in a single-stage ML model.

First ML model 614 may include a predictive regression model simulatinga correlation between the readily known anthropometric parameters 602and the extended anthropometric parameters 608. Extended anthropometricparameters 608 output by first ML model 614 may include theanthropometric inputs 602 and one or more additional physical attributesor metrics that correlate to the entered data. The additional physicalattributes can be any of the secondary physical attributes describedabove. For example, in addition to height 202, and/or gender 207,extended anthropometric parameters 608 can include upper arm length 212,forearm length 214, upper leg length 218, lower leg length 220, footlength 208, or foot width 206 (foot metrics, for example, may be derivedfrom shoe size 204). Training and testing data for the first ML model614 may come from measurements of surgeons and volunteers, as well asfrom open anthropometric datasets, e.g., datasets published on theInternet.

At operation 416, second ML model 616 is used to generate aconfiguration of user console 120 based on extended anthropometricparameters 608. Second ML model 616 can include a predictive regressionmodel simulating a correlation between extended anthropometricparameters of users and their preferences in user consoleconfigurations. For example, the predictive regression model cansimulate a correlation between extended anthropometric parameters 608 ofuser 107 and the preferred user console configuration of user 107.

In an embodiment, second ML model 616 correlates extended anthropometricparameters 608 to console configuration parameters 604. Second ML model616 may be similar to the single-stage ML model 506 illustrated in FIG.5. More particularly, second ML model 616 can be trained to predictoptimal configuration parameters 604, e.g., a display lift parameter318, display translation parameter 320, display tilt parameter 322,pedal tilt parameter 326, pedal translation parameter 324, seattranslation parameter 306, seat recline parameter 312, or seat liftparameter 308, based on the extended set of anthropometric inputs 608,e.g., height 202, gender 207, upper arm length 212, forearm length 214,upper leg length 218, lower leg length 220, foot length 208, or footwidth 206 of user 107. As similarly described above with respect tooperation 404 of FIG. 4A, the recommended configuration of user console120 includes a seating pose of user 107, e.g., a pose of seat 122 and/orother components of user console 120. For example, the configuration caninclude positions and angles of seat 122, display 128, and/or pedals124.

As similarly described above with respect to operation 406 of FIG. 4A,at operation 418 of FIG. 4B one or more actuators of user console 120can be actuated based on parameters 604 to adjust at least one of seat122, display 128, and/or pedals 122 to effect the generated recommendedconfiguration of user console 120. For example, actuators connected toseat 122, display 128, and/or pedals 122 can be controlled to adjust theuser console components to the recommended configuration.

Referring to FIG. 7, a schematic view of a user 107 in a reclined poseat the user console of the surgical robotic system is shown inaccordance with an embodiment. At operation 406 of FIG. 4A, or operation418 of FIG. 4B, seat 122 (and/or other console components) is adjustedto initial console configuration 304 that is predicted to be an optimalstarting position for user 107. Seat actuators 132 can be coupled to theprocessor, and the processor may transmit the set of consoleconfiguration parameters 504 (or 604) generated by ML model 506 (or 606)to drive seat actuators 132. In response to receiving the set ofparameters 504 (or 604) from processor, console actuators canautomatically adjust user console 120 to an initial consoleconfiguration 304, which is defined by the configuration parameters ofactuation output signals 504 (or 604).

Initial console configuration 304 establishes the relative positionbetween user 107 and surgical robotic system 100. For example, therelative position may include a relative orientation of face 302 of user107 and display 128 of surgical robotic system 100. That relativeorientation can include an initial viewing distance 330 or viewing angle332, as described above. Initial console configuration 304 is apredicted optimal starting position for user 107 based on the modeledanthropometric data. The ML model may be trained using data collectedfrom a substantial population, and thus, the predicted optimal startingposition may be an ideal long-term position for user 107. The ML model,however, may not take into account every physical attribute of user 107,and thus, user 107 may want to adjust a component of surgical roboticsystem 100 after user console 120 has been moved into initial consoleconfiguration 304.

Adjustments to the console configuration and retraining of ML modelbased on such adjustments are described below with reference to FIG. 4B.It will be appreciated, however, that such operations may be appended tothe method of FIG. 4A. At operation 420, the processor of surgicalrobotic system 100 detects a manual adjustment to at least one of seat122, display 128, or pedals 124 of user console 120. The manualadjustment can be made by user 107 to effect a new configuration,different than the recommended configuration generated by the MLlearning model(s). For example, the manual adjustment may be of seat 122to an adjusted position 702 from initial console configuration 304. User107 may prefer a different console configuration to the initial consoleconfiguration 304. User 107 may want to tilt display 128, raise pedals124, or tilt seat 122. For example, user 107 may manually adjust seattilt parameter 310 to a position that the user finds more comfortable.Similarly, user 107 can adjust display tilt parameter 322 or pedaltranslation parameter 324 to a more comfortable position.

At operation 422, the processor of surgical robotic system 100 mayretrain one or more of the ML learning models that generated therecommended configuration. For example, in a single-stage model, MLlearning model 506 may be retrained. In a multi-stage model, second MLmodel 616 may be retrained. The retraining may be based on the newconfiguration that user 107 adjusted user console 120 to. Retraining theML models, e.g., second ML model 616, may occur automatically inresponse to detecting the manual adjustment made by user 107 to theconsole configuration. Alternatively, the configuration parametersdefining adjusted position 702 can be saved, e.g., stored in a memory ofuser console 120, for future access. More particularly, informationabout manual adjustments made by one or more users after configuringconsole in the recommended initial configuration can be used to enrichthe training data, which may cause an update to the recommended consoleconfiguration parameters 504 based on the same anthropometric inputs502. The enriched training data can relate a specific set of inputs 502(or 602) to a specific user preference that is different than apredicted user preference, and accordingly, the updated ML model maygenerate a different set of configuration parameters based onanthropometric inputs 502, which improve the prediction. On the otherhand, the adjustment may not be to every parameter, and accordingly, theupdated ML model may generate the same configuration parameters for atleast some of the parameters, which reinforces the prediction in someaspects.

Referring to FIG. 8, a schematic view of the user 107 in a standing pose802 at the user console of the surgical robotic system is shown inaccordance with an embodiment. User 107 can choose from various posturalschemes within which respective initial console configurations 304 maybe found. Surgical robotic system 100 may operate in one or more of aseated mode, a reclined mode, or an elevated mode. For example, the poseof seat 122 can include one of an elevated pose, a seated pose, or areclined pose. An example of surgical robotic system 100 operating inthe seated mode is illustrated in FIG. 3. In the seated mode, user 107is supported by seat 122 in a sitting position in which the thighs ofuser 107 are generally parallel to the ground and the back of user 107is upright. An example of surgical robotic system 100 operating in thereclined mode is illustrated in FIG. 7. In the reclined mode, user 107is supported by seat 122 in a reclined position in which a medial axisof user 107 is tilted toward the ground. An example of surgical roboticsystem 100 operating in the elevated mode is illustrated in FIG. 8. Inthe elevated mode, the user 107 is supported by the seat 122 in astanding position 802 in which user 107 bears weight on his feet.Standing position 802 can be further characterized by the sittingsurface of seat 122 having seat tilt parameter 310 at an angle thatcauses the sitting surface to face in a forward direction, e.g., towarddisplay 128.

Referring to FIG. 9, a block diagram of a computer portion of a surgicalrobotic system is shown in accordance with an embodiment. Surgicalrobotic system 100 can include UID(s) 126, user console 120 havingcomputer system 110, and a robot including robotic components 104, 112.Computer system 110 and UID 126 have circuitry suited to specificfunctionality, and thus, the diagrammed circuitry is provided by way ofexample and not limitation.

One or more processors of user console 120 can control portions ofsurgical robotic system 100, e.g., surgical robotic arms 112 and/orsurgical tools 104. UID 126 may be communicatively coupled to computersystem 110 and/or surgical robotic system 100 to provide input commandsthat are processed by one or more processors of system 100 to controlmovement of surgical robotic arm 112 and/or surgical tool 104 mounted onthe arm. For example, UID 126 may communicate electrical command signals906, e.g., spatial state signals generated by one or more UIDprocessor(s) in response to signals from tracking sensor(s). Spatialstate signals may also be generated by one or more displacement sensorsconnected to the UID processor(s), and the UID processor(s) canelectrically communicate with other input sensors such as drop detectionsensors that generate control signals when the UID 126 is dropped. Theelectrical signals output by the UID processor(s) in response to thesensor tracking signals may be input to cause (or pause) motion ofsurgical robotic system 100.

The input electrical signals 906 may be transmitted by the UIDprocessor(s) to one or more console processors 902 of computer system110 via a wired or wireless connection. For example, UID 126 maytransmit the command signals 906 to console processor(s) 902 viaelectrical wire. Alternatively, UID 126 may transmit command signals 906to console processor(s) 902 via a wireless communication link. Thewireless communication link may be established by respective RFcircuitry of user console 120 and UID 126. The wireless communicationcan be via radiofrequency signals, e.g., Wi-Fi or short range signalsand/or suitable wireless communication protocols such as Bluetooth.

Console processor(s) 902 of computer system 110 may execute instructionsto carry out the different functions and capabilities described above.Instructions executed by console processor(s) 902 of user console 120may be retrieved from a local memory (not shown), which may include anon-transitory machine-readable medium. The instructions may be in theform of an operating system program having device drivers to controlcomponents of user console 120, e.g., seat actuator(s) 132 coupled toseat 122.

In an embodiment, console processor 902 controls components of userconsole 120. For example, one or more seat actuators 132 can receivecommands from console processor(s) 902 to control movement of seat 122.Commands from console processor(s) 902 may also control movement ofdisplay 128 or pedals 124 via corresponding actuators. The controlledactuators, e.g., seat actuator(s) 132, can move respective components,e.g., seat 122, in one or more degrees of freedom, such asforward/backward translation, tilt, vertical position, etc. Consoleprocessor 902 can also transmit video data for presentation on display128. Accordingly, console processor(s) 902 can control operation of userconsole 120. Input commands to seat actuator(s) 132 or console processor902 can be entered by the user via foot pedals 124 or another inputdevice 911 such as a keyboard or a joystick.

User console 120 can control the console components to establish initialconsole configuration 304 as described above. User 107 can enteranthropometric inputs 502 (or 602) into the computer system using one ormore input device 911, such as a keyboard, and/or a graphical userinterface presented by display 128. Console processor 902 can generateconsole configuration parameters 504 (or 604) based on the inputs usingML model 506 (or 606), and drive console actuators (e.g., seat actuators132) to adjust one or more of seat 122, display 128, or pedals 124 intothe optimal initial configuration 304.

Console processor 902 can output tracking signals 908 to othercomponents of surgical robotic system 100 via a link. Tracking signals908 may be transmitted and used to control movement of a robot ofsurgical robotic system 100. In an embodiment, user console 120 iscommunicatively coupled to downstream components of surgical roboticsystem 100, e.g., control tower 130, via wired or wireless links. Thelinks can transmit tracking signals 908 to one or more surgical systemprocessor(s) 912. For example, at least one processor 912 can be locatedin control tower 130, and may be communicatively coupled to systemcomponents, such as robotic arms 112, surgical tools 104, surgicalrobotic platform 111, or one or more displays 920. Actuators 114 ofsurgical robotic system 100 may receive control signals from surgicalsystem processor(s) 912 to cause movement of arm 112 and/or tool 104corresponding to tracking signals 908 based on movement of UID 126.

In the foregoing specification, the invention has been described withreference to specific exemplary embodiments thereof. It will be evidentthat various modifications may be made thereto without departing fromthe broader spirit and scope of the invention as set forth in thefollowing claims. The specification and drawings are, accordingly, to beregarded in an illustrative sense rather than a restrictive sense.

What is claimed is:
 1. A user console system of a surgical robot,comprising: a processor configured to: process an input comprising oneor more known anthropometric parameters of a user, generate an extendedset of anthropometric parameters of the user based on the input using afirst regression model, and generate an initial configuration of theuser console based on the extended set of anthropometric parametersusing a second regression model; and one or more actuators coupled tothe processor, a seat, and a display, wherein the one or more actuatorsare configured to automatically actuate the user console to adjust theseat and the display to the initial configuration for the user.
 2. Theuser console system of claim 1, further comprising one or more pedals.3. The user console system of claim 2, wherein the initial configurationof the user console is represented by one or more parameters including aseat translation, a seat lift, a seat tilt, a seat recline, a displaytranslation, a display lift, a display tilt, a pedal translation, or apedal tilt.
 4. The user console system of claim 1, wherein the one ormore known anthropometric parameters include a gender or a shoe size ofthe user.
 5. The user console system of claim 1, wherein the extendedset of anthropometric parameters correspond to non-postural physicalattributes of the user and include one or more of a height, a gender, anupper arm length, a forearm length, an upper leg length, a lower leglength, a foot width, or a foot length of the user.
 6. The user consolesystem of claim 1, wherein the first regression model simulates acorrelation between the one or more known anthropometric parameters andthe extended set of anthropometric parameters of users, and the secondregression model simulates a correlation between the extended set ofanthropometric parameters of users and their preferences in user consoleconfigurations.
 7. A user console of a surgical robotic system,comprising: a seat; a display; one or more actuators coupled to the seatand the display; and a processor configured to: receive an inputincluding a plurality of physical attributes of a user, process theinput using a machine learning model to generate a set of actuationcommands for adjusting the user console to a recommended configurationbased on the plurality of physical attributes of the user, wherein therecommended configuration effects a seating pose of the user at the userconsole, and a relative viewing distance and angle between the user andthe display, and actuate the one or more actuators of the user consolebased on the set of actuation commands to adjust the seat and thedisplay.
 8. The user console of claim 7, wherein the plurality ofphysical attributes a gender or a shoe size of the user.
 9. The userconsole of claim 7, wherein the machine learning model includes aregularized regression model that recommends a configuration of the userconsole based on a correlation between the plurality of physicalattributes of the user and user preferences in user consoleconfigurations.
 10. The user console of claim 7, wherein adjusting theseat and the display comprises adjusting one or more parametersincluding a seat translation, a seat lift, a seat tilt, a seat recline,a display translation, a display lift, or a display tilt.
 11. A methodof adjusting configurations of a user console of a surgical roboticsystem, the method comprising: receiving one or more anthropometricparameters of a user to tele-operate a surgical robot at a user console,the user console having a seat, a display, and one or more pedals;generating, by a processor, a plurality of extended anthropometricparameters of the user based on the received anthropometric parametersusing a first machine learning model; generating, by the processor, aconfiguration of the user console based on the plurality of extendedanthropometric parameters using a second machine learning model, theconfiguration of the user console comprising a pose of the seat, andpositions and angles of the display and the one or more pedals; andadjusting at least one of the seat, the display, or the one or morepedals to effect the generated configuration of the user console. 12.The method of claim 11, wherein the one or more anthropometricparameters are received from the user.
 13. The method of claim 11,wherein the received anthropometric parameters comprise at least two ofa height, a gender, or a shoe size that are readily known to the user.14. The method of claim 13, wherein the plurality of extendedanthropometric parameters include one or more of a height, a gender, anupper arm length, a forearm length, an upper leg length, a lower leglength, a foot width, or a foot length of the user.
 15. The method ofclaim 14, wherein the first machine learning model comprises apredictive regression model simulating a correlation between the readilyknown anthropometric parameters and the plurality of extendedanthropometric parameters.
 16. The method of claim 11, wherein thesecond regression model comprises a predictive regression modelsimulating a correlation between the plurality of extendedanthropometric parameters of the user and user preferences in userconsole configurations.
 17. The method of claim 11, wherein the pose ofthe seat includes an elevated pose, a seated pose, or a reclined pose.18. The method of claim 17, wherein the pose of the seat is representedby one or more parameters including a seat translation, a seat lift, aseat tilt, or a seat recline.
 19. The method of claim 11, whereinadjusting the user console comprises actuating, by the processor, one ormore actuators of the user console to adjust the pose of the seat, andthe positions and angles of the display and the one or more pedalsaccording to the generated configuration of the user console.
 20. Themethod of claim 11, further comprising: detecting a manual adjustment toat least one of the seat, the display, or the one or more pedals of theuser console to effect a new configuration by the user; and retraining,by the processor, the second machine learning model based on the newconfiguration.